Joel Oskarsson

Selected publications

1. 

   Graph-based Neural Weather Prediction for Limited Area Modeling

   Joel Oskarsson, Tomas Landelius, and Fredrik Lindsten

   In NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning 2023

   Abs Bib PDF Code Poster

   The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming increasingly vital. While most existing Neural Weather Prediction (NeurWP) methods focus on global forecasting, an important question is how these techniques can be applied to limited area modeling. In this work we adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region.

   @inproceedings{oskarsson2023graphbased,
     title = {Graph-based Neural Weather Prediction for Limited Area Modeling},
     author = {Oskarsson, Joel and Landelius, Tomas and Lindsten, Fredrik},
     booktitle = {NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning},
     year = {2023},
   }

2. 

   Temporal Graph Neural Networks for Irregular Data

   Joel Oskarsson, Per Sidén, and Fredrik Lindsten

   In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics 2023

   Abs Bib PDF Video Code Poster Slides

   This paper proposes a temporal graph neural network model for forecasting of graph-structured irregularly observed time series. Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph. This is achieved by introducing a time-continuous latent state in each node, following a linear Ordinary Differential Equation (ODE) defined by the output of a Gated Recurrent Unit (GRU). The ODE has an explicit solution as a combination of exponential decay and periodic dynamics. Observations in the graph neighborhood are taken into account by integrating graph neural network layers in both the GRU state update and predictive model. The time-continuous dynamics additionally enable the model to make predictions at arbitrary time steps. We propose a loss function that leverages this and allows for training the model for forecasting over different time horizons. Experiments on simulated data and real-world data from traffic and climate modeling validate the usefulness of both the graph structure and time-continuous dynamics in settings with irregular observations.

   @inproceedings{tgnn4i,
     title = {Temporal Graph Neural Networks for Irregular Data},
     author = {Oskarsson, Joel and Sid\'en, Per and Lindsten, Fredrik},
     booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
     pages = {4515--4531},
     year = {2023},
     editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
     volume = {206},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
   }

3. 

   Scalable Deep Gaussian Markov Random Fields for General Graphs

   Joel Oskarsson, Per Sidén, and Fredrik Lindsten

   In Proceedings of the 39th International Conference on Machine Learning 2022

   Abs Bib PDF Video Code Poster Slides

   Machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields (GMRFs) provides a principled way to define Gaussian models on graphs by utilizing their sparsity structure. We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only. By designing a new type of layer we enable the model to scale to large graphs. The layer is constructed to allow for efficient training using variational inference and existing software frameworks for Graph Neural Networks. For a Gaussian likelihood, close to exact Bayesian inference is available for the latent field. This allows for making predictions with accompanying uncertainty estimates. The usefulness of the proposed model is verified by experiments on a number of synthetic and real world datasets, where it compares favorably to other both Bayesian and deep learning methods.

   @inproceedings{pmlr-v162-oskarsson22a,
     title = {Scalable Deep {G}aussian {M}arkov Random Fields for General Graphs},
     author = {Oskarsson, Joel and Sid{\'e}n, Per and Lindsten, Fredrik},
     booktitle = {Proceedings of the 39th International Conference on Machine Learning},
     pages = {17117--17137},
     year = {2022},
     editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
     volume = {162},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
   }